API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access
CoRR(2024)
摘要
This study aims to address the pervasive challenge of quantifying uncertainty
in large language models (LLMs) without logit-access. Conformal Prediction
(CP), known for its model-agnostic and distribution-free features, is a desired
approach for various LLMs and data distributions. However, existing CP methods
for LLMs typically assume access to the logits, which are unavailable for some
API-only LLMs. In addition, logits are known to be miscalibrated, potentially
leading to degraded CP performance. To tackle these challenges, we introduce a
novel CP method that (1) is tailored for API-only LLMs without logit-access;
(2) minimizes the size of prediction sets; and (3) ensures a statistical
guarantee of the user-defined coverage. The core idea of this approach is to
formulate nonconformity measures using both coarse-grained (i.e., sample
frequency) and fine-grained uncertainty notions (e.g., semantic similarity).
Experimental results on both close-ended and open-ended Question Answering
tasks show our approach can mostly outperform the logit-based CP baselines.
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